Bi-level variable selection in semiparametric transformation mixture cure models for right-censored data
We investigate the bi-level variable selection problem in semiparametric transformation mixture cure models (STMCM). In this type of mixture cure models, we consider a class of semiparametric transformation models for the conditional survival function and a logistic regression for the incidence comp...
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| Published in | Communications in statistics. Simulation and computation Vol. 52; no. 7; pp. 3006 - 3025 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Philadelphia
Taylor & Francis
03.07.2023
Taylor & Francis Ltd |
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| ISSN | 0361-0918 1532-4141 |
| DOI | 10.1080/03610918.2021.1926499 |
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| Abstract | We investigate the bi-level variable selection problem in semiparametric transformation mixture cure models (STMCM). In this type of mixture cure models, we consider a class of semiparametric transformation models for the conditional survival function and a logistic regression for the incidence component, then conduct group variable selection. The group bridge penalty is adopted for bi-level variable selection on both parts of the mixture cure models. Through simulation studies and real data analyses, we show that the proposed method can identify the important variables and groups that contribute to the cure proportion and the survival function for the uncured subjects, respectively. |
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| AbstractList | We investigate the bi-level variable selection problem in semiparametric transformation mixture cure models (STMCM). In this type of mixture cure models, we consider a class of semiparametric transformation models for the conditional survival function and a logistic regression for the incidence component, then conduct group variable selection. The group bridge penalty is adopted for bi-level variable selection on both parts of the mixture cure models. Through simulation studies and real data analyses, we show that the proposed method can identify the important variables and groups that contribute to the cure proportion and the survival function for the uncured subjects, respectively. |
| Author | Zhong, Wenyan Wu, Jingjing Lu, Xuewen |
| Author_xml | – sequence: 1 givenname: Jingjing orcidid: 0000-0003-4555-1490 surname: Wu fullname: Wu, Jingjing organization: Department of Mathematics and Statistics, University of Calgary – sequence: 2 givenname: Xuewen surname: Lu fullname: Lu, Xuewen organization: Department of Mathematics and Statistics, University of Calgary – sequence: 3 givenname: Wenyan surname: Zhong fullname: Zhong, Wenyan organization: Department of Biostatistics and Research Decision Sciences, MSD China |
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| Cites_doi | 10.1093/biomet/ast029 10.1093/biomet/93.3.627 10.1002/sim.3358 10.4310/SII.2009.v2.n3.a10 10.1111/j.1369-7412.2007.00606.x 10.5705/ss.2013.061 10.1093/biomet/asp020 10.1111/j.1467-9868.2005.00532.x 10.1214/07-AOS584 10.1007/s10985-007-9042-4 10.1093/biomet/91.2.331 10.1111/biom.12300 10.1002/sim.5378 10.1111/j.2517-6161.1996.tb02080.x 10.1093/biomet/asp016 10.2307/3314804 10.1016/j.csda.2012.02.023 10.1214/009053604000000256 10.1093/biomet/79.3.531 10.1111/j.0006-341X.2000.00237.x 10.1111/j.0006-341X.2000.00227.x 10.1198/016214506000001239 |
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| Snippet | We investigate the bi-level variable selection problem in semiparametric transformation mixture cure models (STMCM). In this type of mixture cure models, we... |
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| SubjectTerms | Bi-level variable selection Censored data (mathematics) Group bridge Mixture cure model Mixtures Penalized regression Primary 62N01 secondary 62J07 Semiparametric transformation model Survival Transformations |
| Title | Bi-level variable selection in semiparametric transformation mixture cure models for right-censored data |
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